crime

The crime dataset contains crime reports for Houston from January-August 2010, geocoded with Google Maps.

tibble(crime)

Lots of useful info: dates, types of crimes, locations by type of place, locations by street, locations by longitude/latitude …

First, let’s get an overview of the crimes on the map. The qmplot is often recommended as a quick way to do mapping (but we will switch to another approach shortly). We put in longitude (lon) and latitude (lat) for the x and y parameters and specify crime as the data set. This plots all of the crimes in the database.

(Example adapted from here.)

q1 <- qmplot(lon, lat, data = crime,
             maptype = "toner-lite",
             ## for q* plots need to use I() for direct specifications
             colour = I("red"),
             size = I(0.9),
             alpha=I(.3))

A slightly slower but safer method is to get the map first, then combine it with the data. This way we can retrieve the map and store it; this is both more efficient if we’re going to make a bunch of plots with the same map (almost inevitable if we’re polishing a data visualization), and safer (in case the server goes down/network connection is lost/etc.).

## utility function: extract appropriate components for retrieving a Stamen/OSM map
get_mapzone <- function(data, latcol="lat", loncol="lon") {
  lon <- na.omit(data[[loncol]])
  lat <- na.omit(data[[latcol]])
  return(c(left=min(lon),right=max(lon),bottom=min(lat),top=max(lat)))
}
houston_mz <- get_mapzone(crime) ## find boundaries
## retrieve map
houston_map1 <- get_stamenmap(houston_mz,zoom=7, maptype="toner-lite")

## Source : http://tile.stamen.com/toner-lite/7/28/49.png

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Now we can combine the map with the data set:

(ggmap(houston_map1)
  + geom_point(data=crime,colour="red",size=0.9,alpha=0.3)
)

## Warning: Removed 5 rows containing missing values (geom_point).

This graph is not very good, in particular because

Reduce crime to violent crimes in downtown Houston:

violent_crime <-
  (crime
    %>% filter(
          !offense %in% c("auto theft", "theft", "burglary"),
          -95.39681 <= lon & lon <= -95.34188,
          29.73631 <= lat & lat <=  29.78400
        )
    %>% mutate(
          ## drops unused levels, mitigates downstream errors
          offense = fct_drop(offense), 
          offense = fct_relevel(offense,
               c("robbery", "aggravated assault", "rape", "murder")
               )
        )
  )

houston_map2 <- get_stamenmap(get_mapzone(violent_crime), maptype="toner-lite",
                              zoom=14)

## Source : http://tile.stamen.com/toner-lite/14/3850/6770.png

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(ggmap(houston_map2)
  + geom_point(data = violent_crime, colour = "red",size = 0.9, alpha=.3)
)

(ggmap(houston_map2)
  + geom_density2d(data = violent_crime, aes(x=lon,y=lat), col="red")
)

(ggmap(houston_map2)
  + geom_bin2d(data = violent_crime, alpha=0.5)
  + scale_fill_gradient(low="#F0F0FF", high="#131393",
                        trans=scales::log10_trans())
)

## Warning: Removed 7 rows containing missing values (geom_tile).

Heatmap

To make the contour map more useful, we can assign a gradient. Let’s look at the robberies:

houston_map3 <- get_stamenmap(get_mapzone(violent_crime),
                              maptype="toner-background",
                              zoom=14)

## Source : http://tile.stamen.com/toner-background/14/3850/6770.png

## Source : http://tile.stamen.com/toner-background/14/3851/6770.png

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## Source : http://tile.stamen.com/toner-background/14/3851/6773.png

## Source : http://tile.stamen.com/toner-background/14/3852/6773.png

robbery <- violent_crime %>% filter(offense=='robbery')
(ggmap(houston_map3)
  + stat_density_2d(data=robbery,
                    aes(fill = ..level..), geom = "polygon",
                    alpha = .35, colour = NA)
  + scale_fill_gradient2("Robbery\nHeatmap",
                         low = "white", mid = "yellow",
                         high = "red", midpoint = 650)
)

(ggmap(houston_map3,darken=c(0.9,"white")) ## fade map layer
  + geom_point(data = violent_crime, aes(colour = offense, size=offense)) + 
  facet_wrap(~ offense)+
  scale_colour_brewer(palette="Dark2")+
  scale_size_manual(values=c(1,1,2,3))+
  theme(legend.position="none")
)

https://github.com/paleolimbot/blogdown-site/tree/master/content/post/2019-10-21-ridings

spatial examples

Alex Bushby, Aleks Jovic, Ben Bolker

crime

Heatmap